Executing machine learning models in a production environment can be tricky, especially at a major bank where compliance and risk are carefully taken into account. In this talk I explain how, we, at ING (a large bank operating on global scale), execute our Python models in a production environment by building minimal Docker images for python versions.
I will first talk about the possible security risks of running any docker container in a production environment. Then I will talk about ways in which we can make Docker containers more secure by building minimal docker images for Python. Finally I will explain how these docker images are used in practice to serve machine learning models at ING.
Prerequisites: - Some basic knowledge of Docker can be helpful - Some basic understanding of security can be helpful
Goals: - Understand the security risks of running docker containers - Know how to make docker images more secure - How to build secure model serving docker images
Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/